Efficient Conditional Diffusion Model for SAR Despeckling

Speckle noise inherent in Synthetic Aperture Radar (SAR) images severely degrades image quality and hinders downstream tasks such as interpretation and target recognition. Existing despeckling methods, both traditional and deep learning-based, often struggle to balance effective speckle suppression with structural detail preservation. Although Denoising Diffusion Probabilistic Models (DDPMs) have shown remarkable potential for SAR despeckling, their computational overhead from iterative sampling severely limits practical applicability. To mitigate these challenges, this paper proposes the Efficient Conditional Diffusion Model (ECDM) for SAR despeckling. We integrate the cosine noise schedule with a joint variance prediction mechanism, accelerating the inference speed by an order of magnitude while maintaining high denoising quality. Furthermore, we integrate wavelet transforms into the encoder’s downsampling path, enabling adaptive feature fusion across frequency bands to enhance structural fidelity. Experimental results demonstrate that, compared to a baseline diffusion model, our proposed method achieves an approximately 20-fold acceleration in inference and obtains significant improvements in key objective metrics. This work contributes to real-time processing of diffusion models for SAR image enhancement, supporting practical deployment by mitigating prolonged inference in traditional diffusion models through efficient stochastic sampling.